component decomposition is done

docs/02_components/helpers/h03_robust_kernels_spec.md

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+# Robust Kernels Helper
+
+## Interface Definition
+
+**Interface Name**: `IRobustKernels`
+
+### Interface Methods
+
+```python
+class IRobustKernels(ABC):
+    @abstractmethod
+    def huber_loss(self, error: float, threshold: float) -> float:
+        pass
+    
+    @abstractmethod
+    def cauchy_loss(self, error: float, k: float) -> float:
+        pass
+    
+    @abstractmethod
+    def compute_weight(self, error: float, kernel_type: str, params: Dict[str, float]) -> float:
+        pass
+```
+
+## Component Description
+
+Huber/Cauchy loss functions for outlier rejection in optimization.
+
+## API Methods
+
+### `huber_loss(error: float, threshold: float) -> float`
+
+**Description**: Huber robust loss function.
+
+**Formula**:
+```
+if |error| <= threshold:
+    loss = 0.5 * error^2
+else:
+    loss = threshold * (|error| - 0.5 * threshold)
+```
+
+**Purpose**: Quadratic for small errors, linear for large errors (outliers).
+
+---
+
+### `cauchy_loss(error: float, k: float) -> float`
+
+**Description**: Cauchy robust loss function.
+
+**Formula**:
+```
+loss = (k^2 / 2) * log(1 + (error/k)^2)
+```
+
+**Purpose**: More aggressive outlier rejection than Huber.
+
+---
+
+### `compute_weight(error: float, kernel_type: str, params: Dict[str, float]) -> float`
+
+**Description**: Computes robust weight for error.
+
+**Usage**: Factor Graph applies weights to downweight outliers.
+
+## Dependencies
+
+**External**: numpy
+
+## Test Cases
+
+1. Small error → weight ≈ 1.0
+2. Large error (350m outlier) → weight ≈ 0.1 (downweighted)
+3. Huber vs Cauchy → Cauchy more aggressive
+